Heritability of “small‐world” networks in the brain: A graph theoretical analysis of resting‐state EEG functional connectivity

Abstract

Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic features of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchronization likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and average path length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a smallworld organization are viable markers of genetic differences in brain organization.

Heritability of Resting State EEG Functional Connectivity Patterns

“…The heritability of graph parameter L in the alpha and beta frequency bands and CC in the alpha band ranged from 40% to 44%, which was consistent with the heritability for L in the Dutch sample with the same age ranges (age 16 years), where heritability varied from 29% to 58% (Smit et al, 2010). However, Dutch heritability estimates for CC and L in the theta band were 50% and 89% respectively (Smit et al, 2008), whereas in the present sample these were lower at 23% and 40% respectively. The high genetic correlations between SL and average L confirm the sensitivity of L to the overall connectivity strength (Smit et al, 2010).…”

“…A hit is recorded whenever this second signal Y is also in the same state at time points i and j. SL is then defined as the proportion of hits to the total number of recurrences in the X signal and is therefore a number between 0 and 1. Specific parameter settings for m, l, and p ref depend on the frequency band analyzed and reflect similar choices from previous literature (Ponten et al, 2007;Smit et al, 2008Smit et al, , 2010. More details on SL calculation can be found in several other publications (Montez et al, 2006;Posthuma et al, 2005;Stam and van Dijk, 2002;Varela et al, 2001).…”

“…By applying a threshold such that the average number of edges per node (k) was fixed at k = 5, a binary graph was formed. This choice is somewhat arbitrary, but in this case was based on previous literature where comparable graphs were analyzed (Smit et al, 2008(Smit et al, , 2010.…”

Remission of benign epilepsy with rolandic spikes: An EEG-based connectivity study at the onset of the disease and at remission

“…Like LORETA, LSC was computed for 1 Hz VNBs, from 1 to 25 Hz. EEGfC is genetically determined (Smit et al, 2008). It is a real measure of connectivity of the developing brain although the relationship between functional and anatomical connectivity is far from understood (Friston, 2011).…”

Phenotypic and genetic correlations between evoked EEG/ERP measures during the response anticipation period of a delayed response task

“…Second, because variation in EEG/ERP measures tends to be under strong genetic control (e.g., Smit, Posthuma, Boomsma, & de Geus, 2005;Smit, Posthuma, Boomsma, & de Geus, 2007a;Smit, Stam, Posthuma, Boomsma, & de Geus, 2008;van Beijsterveldt & van Baal, 2002), we tested whether heritability of the three ERP/EEG measures could be explained by a common genetic factor. Third, in keeping with the idea that challenges to the system tend to increase genetic variance (de Geus, Kupper, Boomsma, & Snieder, 2007), heritability of the ERP/ EEG measures were examined under two levels of task difficulty: a low and a high memory load condition.…”